def main(unused_argv):
if not tf.gfile.IsDirectory(FLAGS.train_dir):
tf.gfile.MakeDirs(FLAGS.train_dir)
cfg, cfg_summary = get_named_config(FLAGS.model_cfg,
FLAGS.model_cfg_overrides)
with tf.gfile.Open(os.path.join(FLAGS.train_dir, "cfg.txt"), "w") as f:
f.write(cfg_summary)
# Load data
with tf.name_scope("loader"):
feat_dict = load_noteseqs(
FLAGS.dataset_fp,
cfg.train_batch_size,
cfg.train_seq_len,
max_discrete_times=cfg.data_max_discrete_times,
max_discrete_velocities=cfg.data_max_discrete_velocities,
augment_stretch_bounds=cfg.train_augment_stretch_bounds,
augment_transpose_bounds=cfg.train_augment_transpose_bounds,
randomize_chord_order=cfg.data_randomize_chord_order,
repeat=True)
# Summarize data
tf.summary.image(
"piano_roll",
util.discrete_to_piano_roll(util.demidify(feat_dict["midi_pitches"]),
88))
# Build model
with tf.variable_scope("phero_model"):
model_dict = build_genie_model(feat_dict,
cfg,
cfg.train_batch_size,
cfg.train_seq_len,
is_training=True)
# Summarize quantized step embeddings
if cfg.stp_emb_vq:
tf.summary.scalar("codebook_perplexity",
model_dict["stp_emb_vq_codebook_ppl"])
tf.summary.image(
"genie",
util.discrete_to_piano_roll(
model_dict["stp_emb_vq_discrete"],
cfg.stp_emb_vq_codebook_size,
dilation=max(1, 88 // cfg.stp_emb_vq_codebook_size)))
tf.summary.scalar("loss_vqvae", model_dict["stp_emb_vq_loss"])
# Summarize integer-quantized step embeddings
if cfg.stp_emb_iq:
tf.summary.scalar("discrete_perplexity",
model_dict["stp_emb_iq_discrete_ppl"])
tf.summary.scalar("iq_valid_p", model_dict["stp_emb_iq_valid_p"])
tf.summary.image(
"genie",
util.discrete_to_piano_roll(model_dict["stp_emb_iq_discrete"],
cfg.stp_emb_iq_nbins,
dilation=max(
1, 88 // cfg.stp_emb_iq_nbins)))
tf.summary.scalar("loss_iq_range",
model_dict["stp_emb_iq_range_penalty"])
tf.summary.scalar("loss_iq_contour",
model_dict["stp_emb_iq_contour_penalty"])
tf.summary.scalar("loss_iq_deviate",
model_dict["stp_emb_iq_deviate_penalty"])
if cfg.stp_emb_vq or cfg.stp_emb_iq:
tf.summary.scalar("contour_violation", model_dict["contour_violation"])
tf.summary.scalar("deviate_violation", model_dict["deviate_violation"])
# Summarize VAE sequence embeddings
if cfg.seq_emb_vae:
tf.summary.scalar("loss_kl", model_dict["seq_emb_vae_kl"])
# Summarize output
tf.summary.image(
"decoder_scores",
util.discrete_to_piano_roll(model_dict["dec_recons_scores"], 88))
tf.summary.image(
"decoder_preds",
util.discrete_to_piano_roll(model_dict["dec_recons_preds"], 88))
if cfg.dec_pred_velocity:
tf.summary.scalar("loss_recons_velocity",
model_dict["dec_recons_velocity_loss"])
tf.summary.scalar("ppl_recons_velocity",
tf.exp(model_dict["dec_recons_velocity_loss"]))
# Reconstruction loss
tf.summary.scalar("loss_recons", model_dict["dec_recons_loss"])
tf.summary.scalar("ppl_recons", tf.exp(model_dict["dec_recons_loss"]))
# Build hybrid loss
loss = model_dict["dec_recons_loss"]
if cfg.stp_emb_vq and cfg.train_loss_vq_err_scalar > 0:
loss += (cfg.train_loss_vq_err_scalar * model_dict["stp_emb_vq_loss"])
if cfg.stp_emb_iq and cfg.train_loss_iq_range_scalar > 0:
loss += (cfg.train_loss_iq_range_scalar *
model_dict["stp_emb_iq_range_penalty"])
if cfg.stp_emb_iq and cfg.train_loss_iq_contour_scalar > 0:
loss += (cfg.train_loss_iq_contour_scalar *
model_dict["stp_emb_iq_contour_penalty"])
if cfg.stp_emb_iq and cfg.train_loss_iq_deviate_scalar > 0:
loss += (cfg.train_loss_iq_deviate_scalar *
model_dict["stp_emb_iq_deviate_penalty"])
if cfg.seq_emb_vae and cfg.train_loss_vae_kl_scalar > 0:
loss += (cfg.train_loss_vae_kl_scalar * model_dict["seq_emb_vae_kl"])
if cfg.dec_pred_velocity:
loss += model_dict["dec_recons_velocity_loss"]
tf.summary.scalar("loss", loss)
# Construct optimizer
opt = tf.train.AdamOptimizer(learning_rate=cfg.train_lr)
train_op = opt.minimize(loss,
global_step=tf.train.get_or_create_global_step())
# Train
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
save_checkpoint_secs=600,
save_summaries_secs=FLAGS.summary_every_nsecs) as sess:
while True:
sess.run(train_op)
def main(unused_argv):
if not tf.gfile.IsDirectory(FLAGS.train_dir):
tf.gfile.MakeDirs(FLAGS.train_dir)
cfg, cfg_summary = get_named_config(FLAGS.model_cfg,
FLAGS.model_cfg_overrides)
with tf.gfile.Open(os.path.join(FLAGS.train_dir, "cfg.txt"), "w") as f:
f.write(cfg_summary)
# Load data
with tf.name_scope("loader"):
feat_dict = load_noteseqs(
FLAGS.dataset_fp,
cfg.train_batch_size,
cfg.train_seq_len,
max_discrete_times=cfg.data_max_discrete_times,
max_discrete_velocities=cfg.data_max_discrete_velocities,
augment_stretch_bounds=cfg.train_augment_stretch_bounds,
augment_transpose_bounds=cfg.train_augment_transpose_bounds,
randomize_chord_order=cfg.data_randomize_chord_order,
repeat=True)
# Summarize data
tf.summary.image(
"piano_roll",
util.discrete_to_piano_roll(util.demidify(feat_dict["midi_pitches"]), 88))
# Build model
with tf.variable_scope("phero_model"):
model_dict = build_genie_model(
feat_dict,
cfg,
cfg.train_batch_size,
cfg.train_seq_len,
is_training=True)
# Summarize quantized step embeddings
if cfg.stp_emb_vq:
tf.summary.scalar("codebook_perplexity",
model_dict["stp_emb_vq_codebook_ppl"])
tf.summary.image(
"genie",
util.discrete_to_piano_roll(
model_dict["stp_emb_vq_discrete"],
cfg.stp_emb_vq_codebook_size,
dilation=max(1, 88 // cfg.stp_emb_vq_codebook_size)))
tf.summary.scalar("loss_vqvae", model_dict["stp_emb_vq_loss"])
# Summarize integer-quantized step embeddings
if cfg.stp_emb_iq:
tf.summary.scalar("discrete_perplexity",
model_dict["stp_emb_iq_discrete_ppl"])
tf.summary.scalar("iq_valid_p", model_dict["stp_emb_iq_valid_p"])
tf.summary.image(
"genie",
util.discrete_to_piano_roll(
model_dict["stp_emb_iq_discrete"],
cfg.stp_emb_iq_nbins,
dilation=max(1, 88 // cfg.stp_emb_iq_nbins)))
tf.summary.scalar("loss_iq_range", model_dict["stp_emb_iq_range_penalty"])
tf.summary.scalar("loss_iq_contour",
model_dict["stp_emb_iq_contour_penalty"])
tf.summary.scalar("loss_iq_deviate",
model_dict["stp_emb_iq_deviate_penalty"])
if cfg.stp_emb_vq or cfg.stp_emb_iq:
tf.summary.scalar("contour_violation", model_dict["contour_violation"])
tf.summary.scalar("deviate_violation", model_dict["deviate_violation"])
# Summarize VAE sequence embeddings
if cfg.seq_emb_vae:
tf.summary.scalar("loss_kl", model_dict["seq_emb_vae_kl"])
# Summarize output
tf.summary.image(
"decoder_scores",
util.discrete_to_piano_roll(model_dict["dec_recons_scores"], 88))
tf.summary.image(
"decoder_preds",
util.discrete_to_piano_roll(model_dict["dec_recons_preds"], 88))
if cfg.dec_pred_velocity:
tf.summary.scalar("loss_recons_velocity",
model_dict["dec_recons_velocity_loss"])
tf.summary.scalar("ppl_recons_velocity",
tf.exp(model_dict["dec_recons_velocity_loss"]))
# Reconstruction loss
tf.summary.scalar("loss_recons", model_dict["dec_recons_loss"])
tf.summary.scalar("ppl_recons", tf.exp(model_dict["dec_recons_loss"]))
# Build hybrid loss
loss = model_dict["dec_recons_loss"]
if cfg.stp_emb_vq and cfg.train_loss_vq_err_scalar > 0:
loss += (cfg.train_loss_vq_err_scalar * model_dict["stp_emb_vq_loss"])
if cfg.stp_emb_iq and cfg.train_loss_iq_range_scalar > 0:
loss += (
cfg.train_loss_iq_range_scalar * model_dict["stp_emb_iq_range_penalty"])
if cfg.stp_emb_iq and cfg.train_loss_iq_contour_scalar > 0:
loss += (
cfg.train_loss_iq_contour_scalar *
model_dict["stp_emb_iq_contour_penalty"])
if cfg.stp_emb_iq and cfg.train_loss_iq_deviate_scalar > 0:
loss += (
cfg.train_loss_iq_deviate_scalar *
model_dict["stp_emb_iq_deviate_penalty"])
if cfg.seq_emb_vae and cfg.train_loss_vae_kl_scalar > 0:
loss += (cfg.train_loss_vae_kl_scalar * model_dict["seq_emb_vae_kl"])
if cfg.dec_pred_velocity:
loss += model_dict["dec_recons_velocity_loss"]
tf.summary.scalar("loss", loss)
# Construct optimizer
opt = tf.train.AdamOptimizer(learning_rate=cfg.train_lr)
train_op = opt.minimize(
loss, global_step=tf.train.get_or_create_global_step())
# Train
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
save_checkpoint_secs=600,
save_summaries_secs=FLAGS.summary_every_nsecs) as sess:
while True:
sess.run(train_op)